Corticosteroids in Sepsis and Septic Shock: A Systematic Review, Pairwise, and Dose-Response Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To perform a systematic review and meta-analysis to assess the efficacy and safety of corticosteroids in patients with sepsis. DATA SOURCES: We searched PubMed, Embase, and the Cochrane Library, up to January 10, 2023. STUDY SELECTION: We included randomized controlled trials (RCTs) comparing corticosteroids with placebo or standard care with sepsis. DATA EXTRACTION: The critical outcomes of interest included mortality, shock reversal, length of stay in the ICU, and adverse events. DATA ANALYSIS: We performed both a pairwise and dose-response meta-analysis to evaluate the effect of different corticosteroid doses on outcomes. We used Grading of Recommendations Assessment, Development and Evaluation to assess certainty in pooled estimates. DATA SYNTHESIS: We included 45 RCTs involving 9563 patients. Corticosteroids probably reduce short-term mortality (risk ratio [RR], 0.93; 95% CI, 0.88-0.99; moderate certainty) and increase shock reversal at 7 days (RR, 1.24; 95% CI, 1.11-1.38; high certainty). Corticosteroids may have no important effect on duration of ICU stay (mean difference, -0.6 fewer days; 95% CI, 1.48 fewer to 0.27 more; low certainty); however, probably increase the risk of hyperglycemia (RR, 1.13; 95% CI, 1.08-1.18; moderate certainty) and hypernatremia (RR, 1.64; 95% CI, 1.32-2.03; moderate certainty) and may increase the risk of neuromuscular weakness (RR, 1.21; 95% CI, 1.01-1.45; low certainty). The dose-response analysis showed a reduction in mortality with corticosteroids with optimal dosing of approximately 260 mg/d of hydrocortisone (RR, 0.90; 95% CI, 0.83-0.98) or equivalent. CONCLUSIONS: We found that corticosteroids may reduce mortality and increase shock reversal but they may also increase the risk of hyperglycemia, hypernatremia, and neuromuscular weakness. The dose-response analysis indicates optimal dosing is around 260 mg/d of hydrocortisone or equivalent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.031 | 0.061 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".